Causal trees update

[alexander-pv]

Proposed changes

This PR is mainly about causal trees support.

• The architecture of a causal tree implementation was moved to a more modular approach:

1. BaseCausalDecisionTree inherits everything is needed from scikit-learn BaseDecisionTree and modifies fit() method that stores only appropriate checks for causal trees.
2. CausalTreeRegressor now has RegressorMixin and BaseCausalDecisionTree parent classes which makes it fully compatible with scikit-learn.
3. Split criterion was moved to a separate criterion.pyx where CausalRegressionCriterion inherits methods from scikit-learn RegressionCriterion and implements node_value() to save the average of treatment effect for each node. CausalMSE now is a concrete class with impurity computations for causal trees. I also added StandardMSE concrete class which is actually standard MSE criterion from scikit-learn with modified node_value() method. So, now it is easy to add new criteria and see the influence of each criteria on a causal tree fit .

• Details about causal trees:

1. ATE bootstrap confidence intervals calculation in CausalTreeRegressor now has multiprocessing support.
2. Now you can plot CausalTreeRegressor with standard scikit-learn function.
3. For a deeper research CausalTreeRegressor can calculate the number of treatment and control observations in each leaf, _leaves_groups_cnt low-level attribute. Additionally, plot_dist_tree_leaves_values function gives the distribution of ATE in a tree leaves.
4. CausalRandomForestRegressor based on scikit-learn with CausalTreeRegressor as base_estimator.
5. Method calculate_error in CausalRandomForestRegressor calculates unbiased sampling variance. Source.
6. New Jupyter notebook causal_trees_with_synthetic_data.ipynb with CausalTreeRegressor and CausalRandomForestRegressor models.

• Tests:

1. Additional tests: test_causal_trees.py
2. Makefile contains install, build, test, clean. Now you can simply type make test. Cython code compilation is under the hood.
3. setup() function in setup.py now knows about requirements-test.txt dependencies thanks to tests_require parameter. No need to install them manually.

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• Breaking change (fix or feature that would cause existing functionality to not work as expected)
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[jeongyoonlee]

Thanks @alexander-pv for your much needed contribution. We will review the PR soon.

[jeongyoonlee]

Hi, @alexander-pv,

Sorry for my late review. I thought that I left comments earlier but turned out that I didn't submit them and my review was somehow left pending. Code looks good and thanks for the sample notebook and test code which are very comprehensive. One ask is: let's remove min_impurity_split from the code so that we can use latest scikit-learn. Currently it raises "TypeError: init() got an unexpected keyword argument 'min_impurity_split'".

Thanks!

[alexander-pv]

Hi, @jeongyoonlee ,

Thanks for the review, I pushed necessary changes.

I faced the thing that min_impurity_split removal in latest scikit-learn breaks tree building. It turned out that new version of DepthFirstTreeBuilder prevents causal tree from growing new nodes.
See differences: old condition vs new condition.
Since EPSILON constant in original cython file is widely used and it can't be adjusted, I suggest creating builder.pyx with DepthFirstCausalTreeBuilder as DepthFirstTreeBuilder modification (see latest commits). This file can also be an example how to create custom tree builders via subclassing scikit-learn TreeBuilder.

[jeongyoonlee]

LGTM. Thanks again for your contribution. I really appreciate it!

[jeongyoonlee]

Hi @alexander-pv, Py 3.7 test failed with the error as follows:
https://github.com/uber/causalml/runs/7923168250?check_suite_focus=true#step:6:492

Could you please check? If it's something that needs more time, we can merge this PR first and investigate it in a separate thread.

[alexander-pv]

Hi, @jeongyoonlee, It seems that now everything is fixed 👌.

[jeongyoonlee]

Merged! 👍🏻